Q & A with Keith Goldner, Chief Analyst at numberFire

Keith Goldner is the Chief Analyst at numberFire, a sports analytics company designed to empower smarter decision-making. He graduated Magna Cum Laude from Northwestern University with a B.A. in Mathematics. Keith has worked with three NBA franchises performing extensive statistical analysis and data management. In addition, he has worked with ESPN and the Wall Street Journal, contributing analysis across multiple platforms. He is a member of Phi Beta Kappa and his sports analytics research has been featured at conferences across the nation. Keith was recently named to Forbes Magazine’s 30 Under 30 list for young disruptors, innovators and entrepreneurs in the sports industry.

GSABR: How did a math degree from Northwestern, along with the Northwestern University Sports Business and Research group, help you get involved with working for three NBA teams and ESPN?

KG: I don’t believe just having the degree did much for my career, by itself, although Northwestern was and continues to be great to me. I was, however, fortunate enough to do three independent studies while earning my math degree — all three on football analytics. These studies led to the creation of my blog, Drive-By Football (www.drivebyfootball.com) as well as a submission into the Journal of Quantitative Analysis in Sports. Both of these gave me credibility and visibility in the analytics world. The most valuable classes I took were those in the Probability and Stochastic Processes sequence. In truth, the biggest catalyst in terms of my sports analytics career was networking. Once I was given my first opportunity in the NBA, other organizations (including ESPN), were intrigued by my background and work. NUSBR had really just been formed, so I did not gain a significant amount from it; I have, however, made a few significant contacts through the organization, including becoming close with the creator who now works in the MLB.

GSABR: What are some things non-math or statistics majors can do to get a solid analytical background for sports and eventually do the same kind of analytical work that you have done?

KG: The best way to learn is by reading. There is so much good material out there in terms of sports analytics and more is being created each day. If you want to work in baseball, read Moneyball and Tom Tango’s The Book. If you want to work in basketball, read Basketball on Paper by Dean Oliver. If you want to work in football read The Hidden Game of Football, Advanced NFL Stats (www.advancednflstats.com), my blog, and Football Outsiders. 85% of the work I do is basic algebra and another 10% is basic regression techniques that anyone can learn. The biggest challenge — and the reason why people are successful — is being able to ask the right questions, and approach the answers in innovative ways.

GSABR: You’ve had experience with a range of amateur and professional sports. Which ones are the most/least advanced in terms of analytics and how they’re applied? Which ones are easiest/hardest to analyze?

KG: The main sports I have dealt with are baseball, basketball, and football. And they go from easiest to hardest (and most to least advanced) to analyze in that order. Baseball is by far the easiest due to the independent nature of at-bats. Almost everything is one-on-one with enormous sample sizes so everything is quantifiable. With the advances in PITCHf/x and video tracking for fielding, those areas are now quantifiable too. Basketball is tough because of the flow and 5-on-5 nature of the game, but if you break it down into possessions it is more manageable. Now with SportVU video tracking, expect to see some amazing things analytically in the near future. And last — coincidentally my favorite — is football. Football is extremely tough given the fact that there are 22 players on the field, each with different jobs, interacting with each other at once. It is not too hard to analyze team units — offense, defense, special teams — but to quantify individual player performance is difficult. Football needs to be analyzed situationally, though, rather than by using traditional metrics like yards. 10 yards on 3rd-and-15 is far less valuable than 10 yards on 3rd-and-9. It is also important in all sports to combine the data analysis with other methods of evaluation (like traditional scouting). Film study in the NFL will most likely never be thrown out.

GSABR: How similar is your experience as Chief Analyst for numberFire to the work you had previously done? What are some of the things you do on a day-to-day basis?

KG: My experiences have been very similar, mostly because I have the opportunity to dictate what I want to work on. I have a lot more freedom at numberFire, and I also wear a lot more hats — because it is a small company. The biggest thing I’ve taken out of my work so far is learning programming. I have learned both PHP and MySQL since I joined numberFire, which have been extremely helpful in my analysis (I highly recommend learning as much programming as possible — I regret not being a CS major).

On a day-to-day basis, I manage and update our projections (player and game — whatever sports are going on at that time of year). Then, I usually work on longer term projects to improve our projections and create tools to empower smarter decision-making in fantasy sports and sports in general.

The main difference between numberFire and working with professional franchises is that with pro-teams, your primary goal is always to win games. With numberFire, it is more about putting out the best tools and information to help our consumers.

GSABR: Finally, what would you recommend for students looking to break into sports analytics, whether that’s with a team, league or a company like numberFire?

KG: Apart from what I have mentioned already (reading analytical books/sites, learn programming, writing a blog to get your ideas out there), the biggest thing is networking and not being afraid to reach out. Like I mentioned, the majority of the opportunities I’ve had came from my first boss with the Oklahoma City Thunder. He was phenomenal to me in that he introduced me and recommended my work to others in the industry. I’ve also been through periods of cold-emailing tons of teams asking if they have any opportunities — most teams have a formulaic email address. I also recommend attending conferences like the MIT Sloan Sports Analytics Conference as well as more academic ones like the New England Symposium on Statistics in Sports. For the bigger conferences, it is important to have people you know in the industry to introduce you to people, otherwise it will be hard to meet people due to sheer volume. At the smaller conferences like NESSIS, while they won’t have quite the star power, it will be much easier to approach people who actually work in the industry and make contacts.